Grains of Sand or Butterfly Effect: Standing, the Legitimacy of Precedent, and Reflections on \u3cem\u3eHollingsworth\u3c/em\u3e and \u3cem\u3eWindsor\u3c/em\u3e

One test of whether a scholarly work has achieved canonical status is to ask respected scholars in the field which works, setting aside their own, are essential reads. William Fletcher’s article, The Structure of Standing, now in its twenty-fifth year, would almost certainly emerge at the top of any such lists among standing scholars. And yet, while many at this conference have built upon Fletcher’s insights, there remains notable disagreement concerning standing doctrine’s normative foundations. The central dispute concerns whether standing doctrine should be celebrated as furthering a “private-rights,” or instead, condemned as thwarting a “public-rights,” adjudicatory model. In a series of works employing social choice theory, I have presented standing doctrine as furthering a private-rights adjudicatory model. In separate high-profile works, Professors Heather Elliott and Jonathan Siegel have criticized this account, claiming it rests on the “great myth” that the judicial lawmaking is inextricably tied to dispute resolution, with precedent creation merely an incidental byproduct. Instead, Elliott and Siegel contend that the federal judiciary, including especially the Supreme Court, has the primary responsibility of announcing constitutional rules, with case resolutions a justificatory vehicle for performing that task. Siegel further maintains that if, as the social choice model suggests, standing raises the cost to ideological litigants of timing the path of case law to influence developing doctrine, it is no more effective than tossing a “few grains of sand” into the gears of the judicial-lawmaking apparatus. In this Article I respond to these critiques and defend the social choice analysis of standing and the private-rights model on which it rests. First, these and other public-rights scholars fail to appreciate that the private-rights model enhances the normative legitimacy and durability of precedent. If the justification for creating precedent is the present favorable conditions of judicial staffing, then the arguments for respecting the resulting precedent erode when those conditions change, favoring those opposing the precedent. Second, these critiques misread the social choice model of standing to imply that relaxing its limiting conditions undermines the claim that with reasonable assumptions, even if there are no changes in Supreme Court staffing, in the disposition of cases below, in intervening precedent, and in the jurisprudential views of sitting justices, ideological litigants can effect substantive doctrine through favorable case orderings. The opposite is true: Relaxing these limiting conditions has the potential to enhance, not diminish, incentives to manipulate case orderings for maximal doctrinal effect. Third, and finally, expanding the social choice analysis to account for (1) delays in lower federal courts or state courts, (2) the results of changed judicial staffing on the Supreme Court, and (3) the bidirectional nature of constitutional and prudential standing rules more likely generates a butterfly effect, with substantial implications for developing doctrine, than an inconsequential tossing of sand into the works of developing precedent

Thermal Analysis and Modeling of Steady-State Rod Growth During Gas-Phase Solid Freeform Fabrication

An analysis ofthe steady-state growth ofrods during gas-phase solidfreeform fabrication is presented. It is demonstrated that heat transfer controls the evolution ofshape during laser-induced pyrolysis of slender 3-D structures. Insulating and conductive deposit materials were studied, using both simple analytic and numerical simulations to demonstrate how steady-state rod growth is achieved.Mechanical Engineerin

Parameter Estimation in Semi-Linear Models Using a Maximal Invariant Likelihood Function

In this paper, we consider the problem of estimation of semi-linear regression models. Using invariance arguments, Bhowmik and King (2001) have derived the probability density functions of the maximal invariant statistic for the nonlinear component of these models. Using these density functions as likelihood functions allows us to estimate these models in a two-step process. First the nonlinear component parameters are estimated by maximising the maximal invariant likelihood function. Then the nonlinear component, with the parameter values replaced by estimates, is treated as a regressor and ordinary least squares is used to estimate the remaining parameters. We report the results of a simulation study conducted to compare the accuracy of this approach with full maximum likelihood estimation. We find maximising the maximal invariant likelihood function typically results in less biased and lower variance estimates than those from full maximum likelihood.Maximum likelihood estimation, nonlinear modelling, simulation experiment, two-step estimation.

Southern California partyboat sampling study Quarterly Report no. 3

During the period January 1 to March 31, 1976, Department personnel made 139 sampling trips aboard southern California partyboats. A total of 22,122 fishes from 73 species was identified and measured. Otoliths were removed from 1,536 rockfish carcasses representing 31 species for use in age determination. Sampling personnel tagged and released 68 California barracuda, Sphyraena argentea, and 18 sablefish, Anoplopoma fimbria. The five most common species sampled during this period represented approximately 79% of the total number of fishes measured. These were, in order of importance; bocaccio, Sebastes paucispinis; chilipepper, Sebastes goodei; olive rockfish, Sebastes serranoides; greenspotted rockfish, Sebastes chlorostictus; and vermilion rockfish, Sebastes miniatus. Bocaccio alone accounted for 52% of the sampled catch. (15pp.

Multidate mapping of mosquito habitat

LANDSAT data from three overpasses formed the data base for a multidate classification of 15 ground cover categories in the margins of Lewis and Clark Lake, a fresh water impoundment between South Dakota and Nebraska. When scaled to match topographic maps of the area, the ground cover classification maps were used as a general indicator of potential mosquito-breeding habitat by distinguishing productive wetlands areas from nonproductive nonwetlands areas. The 12 channel multidate classification was found to have an accuracy 23% higher than the average of the three single date 4 channel classifications

Bayesian semiparametric GARCH models

This paper aims to investigate a Bayesian sampling approach to parameter estimation in the semiparametric GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This mixture density has the form of a kernel density estimator of the errors with its bandwidth being the standard deviation. The proposed investigation is motivated by the lack of robustness in GARCH models with any parametric assumption of the error density for the purpose of error-density based inference such as value-at-risk (VaR) estimation. The contribution of the paper is to construct the likelihood and posterior of model and bandwidth parameters under the proposed mixture error density, and to forecast the one-step out-of-sample density of asset returns. The resulting VaR measure therefore would be distribution-free. Applying the semiparametric GARCH(1,1) model to daily stock-index returns in eight stock markets, we find that this semiparametric GARCH model is favoured against the GARCH(1,1) model with Student t errors for five indices, and that the GARCH model underestimates VaR compared to its semiparametric counterpart. We also investigate the use and benefit of localized bandwidths in the proposed mixture density of the errors.Bayes factors, kernel-form error density, localized bandwidths, Markov chain Monte Carlo, value-at-risk

Influence Diagnostics in GARCH Processes

Influence diagnostics have become an important tool for statistical analysis since the seminal work by Cook (1986). In this paper we present a curvature-based diagnostic to access local influence of minor perturbations on the modified likelihood displacement in a regression model. Using the proposed diagnostic, we study the local influence in the GARCH model under two perturbation schemes which involve, respectively, model perturbation and data perturbation. We find that the curvature-based diagnostic often provides more information on the local influence being examined than the slope-based diagnostic, especially when the GARCH model is under investigation. An empirical study involving GARCH modeling of the percentage daily returns of the NYSE composite index illustrates the effectiveness of the proposed diagnostic and shows that the curvature-based diagnostic may provide information that cannot be uncovered by the slope-based diagnostic. We find that the effect or influence of each observation is not invariant across different perturbation schemes, thus it is advisable to study the local influence under different perturbation schemes through curvature-based diagnostics.Normal curvature, modified likelihood displacement, GARCH models.

Advances in semantic representation for multiscale biosimulation: a case study in merging models

As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim’s MML)

Using multiple reference ontologies: Managing composite annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models